Enhanced management zones for precision agriculture

ABSTRACT

The present invention is a system and method for agricultural management-zone delineation to be done over broad geographic extents without overly-localized field-specific data. The instant innovation guides precision agricultural sampling and management by delineating enhanced management zones based upon remote sensing and artificial intelligence and combining the two with data derived from an existing countrywide soil survey database. In an embodiment, the instant innovation uses artificial intelligence from multiple sources to provide granular zone detail. Output of the present innovation can be aggregated to produce management zone sizes that have a level of uncertainty compatible with the needs of the customer-farmer and implementable given the capabilities of available equipment.

CLAIM TO PRIORITY

This Non-Provisional application claims under 35 U.S.C. § 120, as aContinuation-In-Part the benefit of the Non-Provisional Application Ser.No. 16/699,292, filed Nov. 29, 2019, Titled “Enhanced Management Zonesfor Precision Agriculture”, which is hereby incorporated by reference inits entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction of the patent document or thepatent disclosure, as it appears in the Patent and Trademark Officepatent file or records, but otherwise reserves all copyright rightswhatsoever.

BACKGROUND

In order to coax increased yield from agricultural plots under theircontrol, farmers have in recent years turned to “precision agriculture”to differentiate areas with varying degrees of fertility or otherproperties within individual fields, and to determine best practices fortargeting fertilizer applications and other management on asite-specific basis. This site-specific application is enabled byvariable rate technology (VRT): precision agriculture equipment to applyinputs such as fertilizer and soil amendments at different rates withina field based on the spatial variability of soil, crops, etc. Oneapproach to precision agriculture is to delineate “management zones”within an individual field. A management zone is an area within a fieldthat has within it similar characteristics amenable to common managementand different from that within other similarly delineated zones in thefield.

Commonly practiced “precision agriculture” makes use of the U.S.Department of Agriculture (USDA) Natural Resources ConservationService's Soil Survey Geodatabase (SSURGO). The data resident in SSURGO,while providing a useful approximation of soil conditions, was neverintended by its originators to be used to guide precision agriculture ordevelop agricultural management zones within a field.

Other management zone delineation techniques tend to be localized andfield-specific, relying on older technology such as proximal sensing (inthe case of soil electrical conductivity), visual pattern recognitionbased upon a small sample (sometimes with a sample size of merely one)of satellite images, topographic analysis, grid soil sampling, cropyield monitor data, and farmer and/or consultant knowledge.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain illustrative embodiments illustrating organization and method ofoperation, together with objects and advantages may be best understoodby reference to the detailed description that follows taken inconjunction with the accompanying drawings in which:

FIG. 1 is a first process-flow diagram showing the reduction of ZoneAttributes and Prescription Recommendations consistent with certainembodiments of the present invention.

FIG. 2 is a second process-flow diagram showing the reduction of ZoneAttributes and Prescription Recommendations consistent with certainembodiments of the present invention.

FIG. 3 is a third process-flow diagram showing the reduction of ZoneAttributes and Prescription Recommendations consistent with certainembodiments of the present invention.

FIG. 4 is a view of a data stack's reduction to a machine-learning-readydata cube consistent with certain embodiments of the present invention.

DETAILED DESCRIPTION

While this invention is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail specific embodiments, with the understanding that the presentdisclosure of such embodiments is to be considered as an example of theprinciples and not intended to limit the invention to the specificembodiments shown and described. In the description below, likereference numerals are used to describe the same, similar orcorresponding parts in the several views of the drawings.

The terms “a” or “an”, as used herein, are defined as one or more thanone. The term “plurality”, as used herein, is defined as two or morethan two. The term “another”, as used herein, is defined as at least asecond or more. The terms “including” and/or “having”, as used herein,are defined as comprising (i.e., open language).

Reference throughout this document to “one embodiment”, “certainembodiments”, “an embodiment” or similar terms means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the presentinvention. Thus, the appearances of such phrases in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments without limitation.

Reference throughout this document to “SSURGO” indicates the U.S.Department of Agriculture Natural Resources Conservation Service's SoilSurvey Geodatabase.

Reference herein to “DEM” or the plural, “DEMs” indicates “digitalelevation model” or “digital elevation models.”

Reference herein to “STM” indicates “soil terrain model.”

Reference herein to “remote sensing” indicates the human and machineacquisition of information without physical contact and includes aerialand satellite imagery and light detection and ranging (hereinafter,“LIDAR”).

Reference herein to learning systems such as “Artificial Intelligence”and/or “Deep Learning” indicates data analysis through the use ofstatistics, classification algorithms, artificial neural networks,machine learning, and/or feature recognition or pattern recognition.

Reference herein to “zone” or “zones” indicates one or more geospatialareas that adhere to pre-established agricultural criteria, suchcriteria, in a non-limiting example, being as simple as latitudinal andlongitudinal coordinates describing the metes and bounds of the zone, oras complex as adherence to particular yield and soil variabilityconditions.

Reference herein to “variable rate technology” indicates technology thatenables a grower to apply different or different rates of agriculturalinputs and management to various spatially defined areas, by way ofnon-limiting example, management zones, within an individual field.

Reference to “ground truth” indicates the collection of past dataregarding crop varieties planted, soil test results, past appliedfertilizers and soil amendments, and actual yields from the planted areafor each crop which presents a true picture of the management and yieldsof a particular geographical area.

In order to maximize crop yield, farmers have for millennia sought tobetter understand not just the conditions in which their plants bestperform, but the conditions provided to their crops by the fields inwhich the seeds of such plants are sown. Clearly, a farmer who bestmatches field conditions to crop needs can be more assured of maximizingor at least, increasing, crop yield.

Recent Precision Agriculture techniques have used technologies todevelop crop management-zone identification and delineation that isoverly localized and field specific. The soil-landscape paradigm oftraditional soil mapping recognizes five factors influencing the spatialdistribution of soil: climate, organisms, relief, parent material, andtime. The most detailed sources of information about the soil parentmaterial, or surficial geology, are the soil survey maps published bythe USDA-NRCS (SSURGO). While useful tools in their day, the soil surveymaps alone do not contain the level of detail necessary to assurefarmers state-of-the-art yields.

Consequently, there is a need for an agricultural technology that allowsmanagement zone delineation to be done over broad geographic extents inthe absence of overly-localized field-specific data. While in anembodiment the instant innovation utilizes the parent materialinformation from SSURGO as an input data layer, it further uses otherdata sources to account for other yield-correlated factors. In a furtherembodiment, the instant innovation uses a reiterative artificialintelligence process with data from multiple sources to provide granularzone detail.

The instant innovation guides precision agricultural sampling andmanagement by delineating enhanced management zones over broadgeographic extents based upon remote sensing, pattern recognition, andartificial intelligence capability comprised of machine learning andstatistical analysis, and combining them all with data derived from anexisting countrywide soil survey database. By delineating enhancedmanagement zones, it is possible for a farmer to substantially reducethe number of soil samples needed to develop prescriptions for soilamendments such as lime and fertilizer. These zones are similarly wellsuited for the selection of crop varieties developed for different soilenvironments. Enhanced management zones can also be useful in guidingmeasures to control weeds, insect pests, and crop diseases. In addition,such zones may prove useful in non-agricultural land management such asforestry and natural resource conservation.

In an embodiment, the instant innovation combines the utilization oftopographic and spectral information. Multi-scale terrain derivativesare generated from high-resolution DEMs based on aerial LIDAR. Thesedigital terrain derivatives include but are not limited to slopegradient, relative elevation, profile curvature, and plan curvature. Inaddition, multi-temporal, multi/hyper-spectral, satellite and aerialimagery is compiled. Extant vegetation and soil indices are calculatedfrom the spectral bands of such imagery. These indices are mathematicalcombinations of imagery bands that have historically proven useful incharacterizing vegetation and soils. The multiple terrain derivativescreated at different analysis scales are used as geospatial layersparallel to the spectral layers in the process of identifying managementzones and predicting soil properties. The soil properties include allidentified co-variants.

In an embodiment, a hyper-dimensional data-cube may be formed to provideinput to the analytical and machine learning processes of the predictivesystem. The hyper-dimensional data-cube may be formed in differentaspects based upon the sensor, imagery, ground truth, analytical index,soil parent, and digital terrain analysis data utilized in the formationof the hyper-dimensional data-cube. In one or more non-limitingexamples, a hyper-dimensional data-cube may be formed utilizing sensordata from remote sensing of field conditions creating multi-scaledigital terrain analysis data combined with soil parent material datainput from SSURGO. Alternatively, the hyper-dimensional data-cube may beformed utilizing imagery band data from satellite and other imagingsystems combined with calculated vegetation and soil indices. Yetanother hyper-dimensional data-cube may be formed through the utilizingsensor data from remote sensing of field conditions creating multi-scaledigital terrain analysis data combined with soil parent material datainput from SSURGO and utilizing imagery band data from satellite andother imaging systems combined with calculated vegetation and soilindices to create a multi-spectrum data-cube. Each data cube may be usedby the system to predict soil zone attributes and providerecommendations for crop planting, zone management, and soilmaintenance.

In a non-limiting example, the data cube may be used as input to ISODATAutilized by the system and to train artificial intelligence algorithmsto predict and/or estimate certain agronomic parameters. The predictedand/or estimated agronomic parameters are combined with ground truth todelineate a set of optimized management zones.

In an embodiment, an unsupervised classification algorithm will be usedfor zone delineation: given multi-scale terrain and multi-spectrallayers (e.g. the data cube) of some agricultural field of interestlayered with parent material data, the algorithm will delineatedifferent agricultural zones based on common within-zone datacharacteristics. The zones can be characterized by relative yield andyield stability and/or variability in georeferenced soil sample testdata. Because the input data is continuous in nature, the prediction ofsoil properties and/or yield is treated as a regression problem and afully-connected neural network is used.

In an embodiment, Artificial Intelligence (AI) (in a non-limitingexample, such as pattern recognition) is applied to the georeferencedcrop yield and soil test data to delineate preliminary management zonesbased on the data cube, regardless of the data-cube aspect employed bythe AI process. Zone delineation is optimized based on a variable zonesize. In a non-limiting example, the optimized zone size may be thegrower's desired minimum zone size. Zone size for each individual growermay be calculated as a function of the minimum area that the grower canor intends to manage using variable rate technology. The variable ratetechnology enables the grower to apply different type or different ratesof agricultural inputs and soil management techniques to variousspatially defined areas, which may be referred to as “management zones”,within an individual field. The zone delineation for each grower maythus be different based upon a combination of the preferred zone size agrower wishes to work with and the management zones predicted andrecommended by the artificial intelligence algorithm. Such optimizationfurther delineates minimum variability within zones and maximalvariability between and among zones.

Python tooling has become very popular in machine learning and theinstant innovation will make use of those tools during neural networkdevelopment for the creation of zone attributes and soil managementrecommendations. Tools like Pandas, Numpy, TensorFlow, and Keras are afew concrete examples of libraries with full support for constructing aneural network and performing the parallelized mathematical operationsneeded to realize the output model that provides the optimized outputfor a grower.

Neural Networks are a type of supervised artificial intelligence (asopposed to unsupervised) that use labeled data: for each input sample,there will be a corresponding output value also called “ground truth”.The input and output, called the Training Set, are related bymathematical equations with unknown coefficients that the network withinany embodiment of the instant innovation must learn. Human trainerssupervise the operation of the Neural Network and provide the guidancenecessary for the Neural Network to identify and learn the coefficientsof the mathematical equations to produce usable results.

Human supervision is utilized to determine how well the network islearning as it iterates over the input and corrects itself against theoutput during training. At this phase, architectural changes are made tothe network to improve its performance. When a human network designerdetermines that the accuracy against the Training Set is sufficient, thenetwork is then tested against the Development Set, a small set of dataseparated out of the Training Set up front, prior to any trainingactivities. If the accuracy of the output against the Development Set isdetermined to be not sufficient, its tuning parameters (calledhyper-parameters) can be adjusted and the network re-run against theDevelopment Set iteratively until accuracy improves. Finally, thenetwork runs against the “Test Set” data, where overfitting andunderfitting to the Development Set by the model can be determined, andanother set of tuning adjustments performed to better generalize orbetter specialize respectively, the model.

In an embodiment, the instant innovation will use a fully-connectedneural network for regression, one network per soil property. Thetraining set is derived from the data cube regardless of the data cubeaspect employed. The values will be scaled to normalize the data values,and each input sample will correspond to one soil test point. Thecorresponding ground truth for each soil test point may be the value ofthe soil property taken from that same location. All data layers willshare the same geographic coordinate system and projection. Duringdevelopment, the corresponding ground truth labels will be based on theinspection of actual yield monitor and georeferenced soil sample testdata collected from the same location, if such data are available from acustomer. Finally, the Training, Development, and Test sets will beassigned. Target accuracy for the network may be set based on customerfeedback. From there, the iterative process previously described maytake place. The final product, the Rx Maker model, will consist of thefinal network architecture, the learned parameters, the learning rate,hyper-parameter values, and any heuristics (e.g. Regularization) thatmay need to be applied for reducing bias and variance error.

In such embodiments, resulting predictions of soil properties can bemade at infinitely high resolution; however, as resolution increases, sodoes uncertainty regarding the accuracy of the predictions. In anon-limiting example, as the management zone size increases, theheterogeneity of the zone may increase, but the increased predictionrange reduces the uncertainty of the respective predictions of soilproperties. In a commercial setting, model output will be aggregated toproduce management zone sizes that have a level of uncertaintycompatible with the needs of the farmer and implementable with thecapabilities of the farmer's equipment.

Aggregation of model output will be achieved, at least in part, usingspatial generalization techniques. These include, but are not limitedto, algorithms that dissolve zone boundaries. Dissolving zone boundariesfacilitates smoothing ragged zone edges and melding small zone patchesand/or inclusions with larger surrounding zones. Smoothing ragged zoneedges and melding small zones or inclusions into larger surroundingzones may permit the system to associate the soil property predictionsof the larger zone across the combined zones comprised of the largerzone and one or more smoothed ragged zones or zone patches andinclusions. In this manner, dissolving zone boundaries may create anaggregated zone comprised of a large zone and one or more zone patchesand/or inclusions.

In a non-limiting example generalization tools provided by the ArcGIStool set provided by ESRI may be utilized to achieve such zonesmoothing. Generalization tools may include, but are not limited to, theAggregate, Boundary Clean, Expand, Majority Filter, Nibble, RegionGroup, Shrink, and Thin tools.

Optimization of agricultural zones may be created through the use ofeach of the data-cube aspects previously discussed. A first solution maybe realized through applying a first hyper-dimensional data-cubecomprising sensor data from remote sensing of field conditions creatingmulti-scale digital terrain analysis data combined with soil parentmaterial data input from SSURGO as input to one or more learningalgorithms, where the learning algorithms are either supervised orunsupervised. Alternatively, a second solution may be realized throughapplying a hyper-dimensional data-cube formed utilizing imagery banddata from satellite and other imaging systems combined with calculatedvegetation and soil indices, once again as input the one or moresupervised or unsupervised learning algorithms. A third solution may berealized another hyper-dimensional data-cube may be formed through theutilizing sensor data from remote sensing of field conditions creatingmulti-scale digital terrain analysis data combined with soil parentmaterial data input from SSURGO and utilizing imagery band data fromsatellite and other imaging systems combined with calculated vegetationand soil indices to create a multi-spectrum data-cube as input to theone or more supervised or unsupervised learning algorithms. The systemmay be requested to create zone attributes, soil predictions, andmanagement recommendations utilizing each aspect of thehyper-dimensional data-cube as input. The system may then produce threesolutions, one from each sub-process utilizing the first, second, orthird data-cube aspect. By employing three sub-processes to delineateoptimized agricultural zones, the instant innovation internally vets itsresults for maximum optimization. Results of the three parallel zonedelineation strategies utilizing the three aspects of the data-cube asinput are compared against one another and the optimal enhancedmanagement zone and recommendation data may be provided to the client.Optimization is achieved through determination of the data-cube thatbest leads to prediction of any particular parameter of interest wherethe input parameter of interest is supplied by a grower or farmmanagement entity.

In a non-limiting example, a system and method for optimizingagricultural zone attributes, zone predictions, and recommendationscomprises at least having a data processor in communication with a dataserver that is in data communication with a user device that is capableof displaying data representations to a user. Utilizing any one of threedata-cube aspects the system may construct a first dataset using remotesensing and digital analysis, construct a second dataset using collectedimagery bands and calculated indices, and/or construct a third datasetusing a combination of inputs to the first dataset and the seconddataset. The system may then apply artificial intelligence algorithms tothe first dataset and output a first set of zone attributes, applyartificial intelligence algorithms to the second dataset and output asecond set of zone attributes, and apply artificial intelligencealgorithms to the third dataset and output a third set of zoneattributes. To determine the optimum set of zone attributes the systemmay compare the first, second, and third sets of zone attributes inlight of the one or more parameters input by the user as guidance. Thesystem may then deliver an optimized set of zone attributes and one ormore zone management recommendations to the user where the set of zoneattributes present the recommendation of zone attributes and/or zonemanagement that optimize the results for the one or more parametersinput by the grower or farm management entity.

Turning now to FIG. 1 , a first process-flow diagram showing thereduction of Zone Attributes and Prescription Recommendations consistentwith certain embodiments of the present invention is shown. Thesub-process of FIG. 1 begins at 100. Remote Sensing of Field Conditions102 is performed via aerial LIDAR, in a non-limiting example. DigitalElevation Models (DEMs) based upon the LIDAR data are subjected toMulti-scale Digital Terrain Analysis (DTA) at 104, which yields a numberof attributes including, by way of non-limiting example, SLOPECURVATURE. These attributes are to be used in subsequent steps. The DTAdata are compiled in the Data Cube (A) 106, along with input Soil ParentMaterial Data 108. The compilation of data is used to train anartificial intelligence Deep Learning Algorithm at 110 to predict orestimate georeferenced crop yield and soil test parameters. Theapplication of Deep Learning Algorithm at 110 produces PrescriptionRecommendations, typically in the non-limiting format example of a tableor map at 114. The application of Deep Learning Algorithm at 110 alsoproduces Zone Attributes (A) to aggregate zones and attributes of zonesat 122. The compiled data of Data Cube (A) 106 along with Soil ParentMaterial Data 108 are also subject to an artificial intelligenceunsupervised classification learning algorithm 112 and processed inorder to delineate preliminary Zones at 116. Zone delineationeffectiveness at 118 is evaluated based on how well the Zones capturethe spatial variability in georeferenced soil-test data and crop yield.Zone delineation effectiveness at 118 may be calculated using realground truth empirical provable data. Alternatively, Zone effectivenessdelineation at 118 may be calculated using Deep Learning output from theapplication of Deep Learning Algorithms at 110. Zone delineation basedon SSURGO map-units is also evaluated. Best among all candidatedelineations are those that: 1) maximize the total number ofstatistically different zones for each soil or crop parameter inquestion (by way of non-limiting example, the instant innovation may beused to make parameter-specific maps, such as a map for phosphorus thatis different from a map for potassium, which in turn will be differentfrom a map for organic matter, etc.) 2) maximize inter-zone differencesin these parameters; and 3) minimize the sum of area-weightedwithin-zone variances. The data for these evaluations are actualgeoreferenced soil-test and yield data when available. When notavailable, the predictions from the artificial intelligence predictionalgorithm are used. “Virtual agronomic effectiveness” of a delineationis based on the extent to which inter-zone differences are great enoughto warrant differential variable-rate management. These judgements aremade based on: 1) the likelihood of a response to differentialmanagement that is within the capabilities of the grower's variable rateapplication equipment; and 2) grower preferences. The systemincorporates management “rules” at 120, and derives Aggregated ZoneAttributes (A) for smoothed and aggregated zones that are consistentwith the pre-established management rules at 122. The sub-process endsat 124.

Turning now to FIG. 2 , a second process-flow diagram describing thereduction of input imagery and index data to Zone Attributes andPrescription Recommendations consistent with certain embodiments of thepresent invention is shown. The sub-process of FIG. 2 , which mayoperate in parallel with the sub-process of FIG. 1 , begins at 202. At204, multi-temporal, multispectral satellite and aerial imagery bandsare collected. At 206 extant vegetation and soil indices are calculatedfrom the collected spectral bands. The indices are mathematicalcombinations of imagery bands that have proven useful in characterizingvegetation and soil. At 208 the spectral imagery bands and calculatedindices are combined into the Data Cube (B) 210. Data Cube (B) 210compiled data are input to at least Deep Learning Algorithms 212 andUnsupervised Learning Algorithms 214.

The compilation of data is used to train an artificial intelligence DeepLearning Algorithm at 212 to predict or estimate georeferenced cropyield and soil test parameters. The application of Deep LearningAlgorithm at 212 produces Prescription Recommendations, typically in thenon-limiting format example of a table or map at 218. The application ofDeep Learning Algorithm at 212 also provides input to the creation ofZone Attributes (B) to aggregate zones and attributes of zones at 224.

The compiled data of Data Cube (B) 210 data are also subject to anartificial intelligence unsupervised classification learning algorithm214 and processed in order to delineate preliminary Zones at 216. Zonedelineation effectiveness at 220 is evaluated based on how well theZones capture the spatial variability in georeferenced soil-test dataand crop yield. Zone delineation effectiveness at 220 may be calculatedusing real ground truth empirical provable data. Alternatively, Zoneeffectiveness delineation at 220 may be calculated using Deep Learningoutput from the application of Deep Learning Algorithms at 212. “Virtualagronomic effectiveness” of a delineation is based on the extent towhich inter-zone differences are great enough to warrant differentialvariable-rate management. These judgements are made based on: 1) thelikelihood of a response to differential management that is within thecapabilities of the grower's variable rate application equipment, and 2)grower preferences. The system incorporates management “rules” at 222,and derives Aggregated Zone Attributes (B) for smoothed and aggregatedzones that are consistent with the pre-established management rules at224. The sub-process ends at 226.

Turning now to FIG. 3 , a third process-flow diagram showing thereduction of Zone Attributes and Prescription Recommendations consistentwith certain embodiments of the present invention is shown. Thesub-process of FIG. 3 begins at 300. At 310, LIDAR is used to provideremote sensing of field conditions. Multi-scale digital terrain analysisderivatives at 312 and Soil Parent Material Data at 314 augment the DataCube (C) at 308 to train artificial intelligence deep learningprediction algorithms at 326 and delineate a third set of optimizedagricultural zones at 324. Deep Learning Algorithms at 326 producePrescription Recommendations at 328. Data Cube (C) 308 inputs data toUnsupervised Learning Algorithms at 316, leading to Zone Delineation at318. At 320 the system evaluates for Zone Effectiveness with input fromthe Deep Learning Algorithms. At 322 the system incorporates management“rules” to determine Aggregated Zone Attributes (C) for smoothed andaggregated zones at 324. At 330 the sub-process ends.

The predictive values of Data Cube (A), Date Cube (B), and Data Cube (C)are compared statistically in order to choose the Data Cube (from theset of Data Cubes A, B, and C) that best leads to prediction of anyparticular parameter of interest.

Turning now to FIG. 4 , a view of a data stack's reduction to amachine-learning-ready data cube consistent with certain embodiments ofthe present invention is shown. Each layer in the Data Layer Stack at402 quantifies a single attribute. Attributes include: 1) topography ascaptured in the digital elevation model (DEM); 2) DEM derivatives suchas the DTA; 3) individual spectral bands, for instance, in anon-limiting example, red, green, blue, near-infrared, hyperspectral, orany other provided image spectral band from satellite imagery; and 4)their derivative vegetation and soil indices. The Data Layer Stack 402is combined to form a Hyper-dimensional Data Cube 404. TheHyper-dimensional Data Cube 404 is used to train artificial intelligenceprediction algorithms at 406 and to delineate Zones via artificialintelligence unsupervised classification algorithms.

While certain illustrative embodiments have been described, it isevident that many alternatives, modifications, permutations, andvariations will become apparent to those skilled in the art in light ofthe foregoing description.

We claim:
 1. A system for optimizing agricultural soil zone attributescomprising: a data processor in communication with a data server; a userdevice capable of displaying data representations to a user; a firstdataset constructed from remote sensing and digital analysis; a seconddataset constructed from collected imagery bands and calculated indices;a third dataset constructed from a combination of inputs to the firstdataset and the second dataset; wherein the data processor appliesartificial intelligence algorithms to the first dataset and outputs afirst set of soil zone attributes; wherein the data processor appliesartificial intelligence algorithms to the second dataset and outputs asecond set of soil zone attributes; wherein the data processor appliesartificial intelligence algorithms to the third dataset and outputs athird set of soil zone attributes; wherein the data processor comparesthe first, second, and third sets of soil zone attributes; wherein thedata processor creates preliminary zone boundaries from the first,second, and third sets of soil zone attributes by (1) maximizing a totalnumber of statistically different soil zone attributes, (2) maximizinginter-zone differences between soil zone attributes, and (3) minimizingwithin-zone variances; wherein the data processor calculates a change inat least one set of soil zone attributes from an aggregated zonecomprised of the preliminary zone and one or more zone patches and/orinclusions; wherein the data processor dissolves zone boundaries of thepreliminary zone and the one or more zone patches and/or inclusions; andwherein the data processor delivers an optimized set of soil zoneattributes and one or more zone management recommendations to the user.2. The system of claim 1, where the remote sensing is achieved withLIDAR.
 3. The system of claim 1, where the first dataset, seconddataset, and third dataset are supplemented by SSURGO Soil ParentMaterial Data.
 4. The system of claim 1 where the artificialintelligence algorithms are Deep Learning algorithms, UnsupervisedLearning algorithms, or a combination of Deep Learning and UnsupervisedLearning algorithms.
 5. The system of claim 1 where an evaluation ofzone effectiveness incorporates ground truth or Deep Learning algorithmoutput.
 6. A method for optimizing agricultural soil zone attributeswith a data processor comprising: constructing a first dataset usingremote sensing and digital analysis; constructing a second dataset usingcollected imagery bands and calculated indices; constructing a thirddataset using a combination of inputs to the first dataset and thesecond dataset; applying artificial intelligence algorithms with thedata processor to the first dataset and outputting a first set of soilzone attributes; applying artificial intelligence algorithms with thedata processor to the second dataset and outputting a second set of soilzone attributes; applying artificial intelligence algorithms with thedata processor to the third dataset and outputting a third set of soilzone attributes; comparing with the data processor the first, second,and third sets of soil zone attributes; creating with the data processorpreliminary zone boundaries from the first, second, and third sets ofsoil zone attributes by (1) maximizing a total number of statisticallydifferent soil zone attributes, (2) maximizing inter-zone differencesbetween soil zone attributes; and (3) minimizing within-zone variances;calculating with the data processor a change in at least one set of soilzone attributes from creating an aggregated zone comprised of thepreliminary zone and one or more zone patches and/or inclusions;dissolving with the data processor zone boundaries of zones of thepreliminary zone and the one or more zone patches and/or inclusions; anddelivering with the data processor an optimized set of soil zoneattributes and one or more zone management recommendations to the user.7. The method of claim 6, where the remote sensing is achieved withLIDAR.
 8. The method of claim 6, where the first dataset, seconddataset, and third dataset are supplemented by SSURGO Soil ParentMaterial Data.
 9. The method of claim 6, where the artificialintelligence algorithms are Deep Learning algorithms, UnsupervisedLearning algorithms, or a combination of Deep Learning and UnsupervisedLearning algorithms.
 10. The method of claim 6, where an evaluation ofzone effectiveness incorporates ground truth or Deep Learning algorithmoutput.